U.S. patent number 6,941,263 [Application Number 09/896,062] was granted by the patent office on 2005-09-06 for frequency domain postfiltering for quality enhancement of coded speech.
This patent grant is currently assigned to Microsoft Corporation. Invention is credited to Vladimir Cuperman, Allen Gersho, Hosam A. Khalil, Hong Wang.
United States Patent |
6,941,263 |
Wang , et al. |
September 6, 2005 |
Frequency domain postfiltering for quality enhancement of coded
speech
Abstract
A method and system of performing postfiltering in the frequency
domain to improve the quality of a speech signal, especially for
synthesized speech resulting from codecs of low bit-rate, is
provided. The method comprises LPC tilt computation and
compensation methods and modules, a formant filter gain computation
method and module, and an anti-aliasing method and module. The
formant filter gain calculation employs an LPC representation, an
all-pole modeling, a non-linear transformation and a phase
computation. The LPC used for deriving the postfilter may be
transmitted from an encoder or may be estimated from a synthesized
or other speech signal in a decoder or receiver. The invention may
be implemented in a linked decoder and encoder. A separate LPC
evaluation unit that is responsible for processing and or deriving
the LPC may be implemented within the invention.
Inventors: |
Wang; Hong (Bellevue, WA),
Cuperman; Vladimir (Goleta, CA), Gersho; Allen (Santa
Barbara, CA), Khalil; Hosam A. (Bellevue, WA) |
Assignee: |
Microsoft Corporation (Redmond,
VA)
|
Family
ID: |
25405563 |
Appl.
No.: |
09/896,062 |
Filed: |
June 29, 2001 |
Current U.S.
Class: |
704/219; 704/225;
704/E19.047; 704/E21.009 |
Current CPC
Class: |
G10L
19/26 (20130101); G10L 21/0364 (20130101) |
Current International
Class: |
G10L
19/00 (20060101); G10L 21/00 (20060101); G10L
19/14 (20060101); G10L 21/02 (20060101); G10L
019/14 () |
Field of
Search: |
;704/225,203,205,206,209,219,224 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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|
1336454 |
|
Jul 1995 |
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CA |
|
0503684 |
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Jul 1998 |
|
EP |
|
2887286 |
|
Jun 1999 |
|
JP |
|
WO 00 11655 |
|
Mar 2000 |
|
WO |
|
Other References
Juin-Hwey et al., "Adaptive postfiltering for quality enhancement
of coded speech," IEEE Trans. SP. vol. 3, No. 1, 1995, pp. 59-71.
.
Azhar Mustapha et al., "An adaptive postfiltering technique based
on the modified Yule-Walker filter," ICASSP-1999, pp. 197-200.
.
H. Tasaki et al., "Post noise smother to improve low bit rate
speech-coding performance," Proceeding of 1999 Workshop on Speech
Coding for Telecommunication, pp. 159-161. .
Roch Lefebvre et al., "Spectral amplitude warping (SAW) for noise
spectrum s haping in audio coding," ICASSP-1997, pp. 335-338. .
H. Tasaki et al., "Spectral postfilter design based on LSP
transformation," Proceeding of 1997 Workshop on Speech Coding for
Telecommunication, pp. 57-58. .
Kabal et al., "Adaptive Postfiltering For Enhancement Of Noisy
Speech In The Frequency Domain," Proceedings of the International
Symposium On Circuits And Systems, IEEE, US, vol. 1 SYMP. 24, pp.
312-315 (Jun. 1991). .
European Search Report..
|
Primary Examiner: Lerner; Martin
Attorney, Agent or Firm: Microsoft Corp.
Claims
What is claimed is:
1. A method of postfiltering a speech signal using linear
predictive coefficients of the speech signal for enhancing human
perceptual quality of the speech signal, the method comprising the
steps of: generating a postfilter by performing a non-linear
transformation the linear predictive coefficients spectrum in the
frequency domain; applying the generated postfilter to the
synthesized speech signal in the frequency domain; and transforming
the filtered frequency domain synthesized speech signal into a
speech signal in the time domain; wherein the step of generating a
postfilter further comprises the steps of: representing the linear
predictive coefficients spectrum by a time domain vector;
transforming the time domain vector into a frequency domain vector
by a Fourier transformation; inversing the frequency domain vector;
and calculating gains according to the magnitude of the all-pole
model vector, wherein the gains include a magnitude and a phase
response.
2. The method of claim 1, wherein the step of calculating the gains
further comprises the steps of: normalizing the magnitude of the
all-pole model vector; conducting a non-linear transformation for
the normalized magnitude of the all-pole model vector to obtain the
magnitude of the gains; estimating the phase response of the gains;
and forming the gains by combining the magnitude and the estimated
phase response of the gains.
3. The method of claim 2, wherein the step of estimating the phase
response further comprises executing a fast Fourier transformation
based phase shifter on the gains.
4. The method of claim 2, wherein the non-linear transformation
function comprises a scaling function with a scaling factor between
0 and 1.
5. The method of claim 1, wherein the step of generating a
postfilter further comprises executing an anti-aliasing procedure
in the time domain after the step of calculating the gains.
6. The method of claim 1, wherein the all-pole model is represented
by a logarithm of the inverse magnitude of the frequency domain
linear predictive coefficients vector.
7. A computer-readable medium having computer-readable instructions
for performing steps to postfilter a synthesized speech signal
using the linear predictive coefficients spectrum of the speech
signal comprising the steps of: computing the tilt of the linear
predictive coefficients spectrum; compensating the linear
predictive coefficients spectrum using the computed tilt;
generating a postfilter by executing a non-linear transformation of
the compensated linear predictive coefficients spectrum in the
frequency domain; and applying the generated postfilter to the
synthesized speech signal in the frequency domain; wherein the step
of generating a postfilter further comprises the steps of:
representing the linear predictive coefficients by a time domain
vector; transforming the time domain vector into a frequency domain
vector by a Fourier transformation; transferring the frequency
domain vector into an all-pole model vector; and calculating gains
according to the magnitude of the all-pole model vector, wherein
the gains include a magnitude and phase response.
8. The computer-readable medium of claim 7, wherein step of
calculating the gains further comprises the steps of: normalizing
the magnitude of the all-pole model vector; conducting a non-linear
transformation for the normalized magnitude of the all-pole model
vector to obtain the magnitude of the gains; estimating the phase
response of the gains; and forming the gains by combining the
magnitude and the estimated phase response of the gains.
9. The computer-readable medium of claim 8, wherein the step of
estimating the phase response further comprises executing a fast
Fourier transformation based phase shifter.
10. The computer-readable media of claim 8, wherein the non-linear
transformation function comprises a scaling function with a scaling
factor between 0 and 1.
11. The computer-readable medium of claim 7, wherein the all-pole
model is represented by a logarithm of the inverse magnitude of the
frequency domain vector.
12. A computer-readable medium having computer-readable
instructions for performing steps to postfilter a synthesized
speech signal using the linear predictive coefficients spectrum of
the speech signal comprising the steps of: computing the tilt of
the linear predictive coefficients spectrum; compensating the
linear predictive coefficients spectrum using the computed tilt;
generating a postfilter by executing a non-linear transformation of
the compensated linear predictive coefficients spectrum in the
frequency domain and executing an anti-aliasing procedure in the
time domain; and applying the generated postfilter to the
synthesized speech signal in the frequency domain.
13. An apparatus for postfiltering a speech signal using a
plurality of linear predictive coefficients of the speech signal
for enhancing human perceptual quality of the speech signal, the
apparatus comprising: a Fourier transformation module operable for
conducting a Fourier transformation; an inverse Fourier
transformation module operable for conducting inverse Fourier
transformation; and a formant filter comprising formant filter
gains, wherein the gains are calculated in the frequency domain by
performing a non-linear transformation of the linear predictive
coefficients; wherein the formant filter further comprises: a
linear predictive coefficients tilt computation module for
computing the tilt of the linear predictive coefficients spectrum;
a linear predictive coefficients tilt compensation module for
compensating the linear predictive coefficients according to the
computed tilt of the linear predictive coefficients spectrum; a
formant gain calculation module for calculating formant filter
gains in the frequency domain by performing a non-linear
transformation of the linear predictive coefficients after tilt
compensation, wherein the gains include a magnitude and phase
response; and a gain application module for applying the format
filter gains to a speech signal by multiplying the gains and the
speech signal in the frequency domain.
14. The apparatus of claim 13, wherein the formant gain calculation
module further comprises: a linear predictive coefficients
representation module for representing the linear predictive
coefficients by a time domain vector; a modeling module for
modeling a frequency domain vector according to a predefined model
for generating a magnitude, wherein the frequency domain vector is
transformed from the time domain vector representing the LPC
coefficients; a linear predictive coefficients non-linear
transformation module for performing a non-linear transformation on
the magnitude and producing the magnitude of the formant filter
gains; a phase computation module for computing a phase response of
the formant filter gains according to the magnitude of the model
after non-linear transformation; a formant filter gain combination
module for combining the magnitude and the phase response of the
formant filter gain; and an anti-aliasing module for preventing
aliasing caused by application of the formant filter.
15. The apparatus of claim 14, wherein the line predictive
coefficients representation module is adapted for representing the
linear predictive coefficients by a zero-padding technique.
16. The apparatus of claim 14, wherein the line predictive
coefficients non-linear transformation module further comprises a
scaling function with a scaling factor of between 0 and 1.
17. The apparatus of claim 14, wherein the phase computation module
further comprises a Hilbert phase shifter in the time domain.
18. An apparatus for use with a postfilter for processing linear
predictive coefficients of a signal and providing a frequency
domain formant filter gains for a formant filter, the apparatus
comprising: a linear predictive coefficients tilt computation
module for computing the tilt of the linear predictive
coefficients; a linear predictive coefficients tilt compensation
module for compensating the linear predictive coefficients spectrum
according to the computed tilt of the linear predictive
coefficients spectrum; and a formant filter gain computation module
for calculating the frequency domain formant filter gains according
to the linear predictive coefficients, wherein the gains include a
magnitude and a phase response.
Description
TECHNICAL FIELD
This invention is related in general to the art of signal filtering
for enhancing the quality of a signal, and more particularly to a
method of postfiltering a synthesized speech signal to provide a
speech signal of improved quality.
BACKGROUND OF THE INVENTION
Electronic signal generation is pervasive in all areas of
electronic and electrical technology. When an electrical signal is
used to emulate, transmit, or reproduce a real world quantity, the
quality of the signal is important. For example, speech is often
received via a microphone or other sound transducer and transformed
into an electrical representation or signal. In addition to the
artificial noise introduced as an artifact of this transformation,
other artificial noise may be additionally introduced into the
signal during transmission, and coding and/or decoding. Such noise
is often audible to humans, and in fact may dominate a reproduced
speech signal to the point of distracting or annoying the
listener.
Speech coders, particularly those operating at low bit rates, tend
to introduce quantization noise that may be audible and thereby
impair the quality of the recovered speech. A postfilter is
generally used to mask noise in coded speech signals by enhancing
the formants and fine structure of such signals. Typically, noise
in strong formant regions of a signal is inaudible, whereas noise
in valley regions between two adjacent formants of a signal is
perceptible since the signal to noise ratio (SNR) in valley regions
is low. The SNR in the valley region may be even lower in the
context of a low bit rate codec, since the prevailing linear
prediction (LP) modeling methods represent the peaks more
accurately than the valleys, and the available bits are
insufficient to adequately represent the signal in the valleys.
Thus, it is desirable that a speech postfilter attenuates the
valleys while preserving the peaks in order to reduce the audible
noise level.
Juin-Hwey Chen et al. have proposed an adaptive postfiltering
algorithm consisting of a pole-zero long-term postfilter cascaded
with a short-term postfilter. The short-term postfilter is derived
from the parameters of the LP model in such a way that it
attenuates the noise in the spectrum valleys. These parameters are
commonly referred to as linear predictive coding coefficients, or
LPC coefficients, or LPC parameters. Additionally, Wang et al.
introduced a frequency domain adaptive postfiltering algorithm to
suppress noise in spectrum valleys. The aforementioned
postfiltering algorithms reduce noise without introducing
substantial spectral distortion, but they are not efficient in
reducing the perceptible noise in shallow, rather than deep,
valleys between formants, especially in the context of low bit-rate
coders such as those operating at below 8 kbps. A primary
explanation for this drawback is that the frequency response of the
postfilter itself does not adequately follow the detailed fine
structure of the spectral envelope, leading to the masking of
shallow valleys between closely-spaced formants.
A typical early time domain LPC postfiltering architecture is
illustrated in FIG. 1. An input bit-stream, perhaps transmitted
from an encoder, is received at decoder 100. A bit-stream decoder
110 associated with decoder 100 decodes the incoming bit-stream.
This step yields a separation of the bit stream into its logical
components or virtual channel contents. For example, the bit stream
decoder 110 separates LPC coefficients from a coded excitation
signal for linear prediction-based codecs. The decoded LPC
coefficients are transmitted to a formant filter 131, which is the
first stage of a time domain postfilter 130. A synthesized speech
signal produced by a speech synthesizer 120 is input to the formant
filter 131 followed by a pitch filter 132 wherein the harmonic
pitch structure of the signal is enhanced. Cascaded with the pitch
filter, a tilt compensation module 133 is generally provided for
removing the background tilt of the formant filter to avoid
undesirable distortion of the postfilter. Finally, a gain control
is applied to the signal in gain controller 134 to eliminate
discontinuity of signal power in adjacent frames.
The frequency response of the postfilter architecture represented
in prior speech postfiltering systems does not adequately follow
the detailed fine structure of the speech spectrum nor does it
always adequately resolve the spectral envelope peaks and
valleys.
SUMMARY OF THE INVENTION
This invention provides a method of postfiltering in the frequency
domain, wherein the postfilter is derived from the LPC spectrum.
Furthermore, for enhancing the spectral structure efficiently, a
non-linear transformation of the LPC spectrum is applied to derive
the postfilter. To avoid uneven spectral distension due to a
nonlinear transformation of the background spectral tilt, tilt
calculation and compensation is preferably conducted prior to
application of the formant postfilter. Finally, to avoid aliasing,
the invention provides an anti-aliasing procedure in the time
domain. Initial implementation results have shown that this method
significantly improves the signal quality, especially for those
portions of the signal attributable to low power regions of the
speech spectrum.
In general, signal filtering of speech and other signals may be
performed in the time domain or the frequency domain. In the time
domain, filter application is equivalent to performing a
convolution combining a vector representative of the signal and a
vector representative of an impulse response of the filter
respectively, to produce a third vector corresponding to the
filtered signal. In contrast, in the frequency domain, the
operation of applying a filter to a signal is equivalent to simple
multiplication of the spectrum of the signal by that of the filter.
Thus, if the spectrum of the filter preserves the spectrum of the
signal in detail, filtering of the signal preserves the fine
structure and formants of the signal. In particular, a valley
present in the speech spectrum will never completely disappear from
the filtered spectrum, nor will it be transformed into a local peak
instead of a valley. This is because the nature of the inventive
postfilter preserves the ordering of the points in the spectrum; a
spectral point that is greater than its neighbor in the pre-filter
spectrum will remain greater in the filtered spectrum, although the
degree of difference between the two may vary due to the
filter.
Thus, the postfilter described herein employs a frequency response
that follows the peaks and valleys of the spectral envelope of the
signal without producing overall spectrum tilt. Such a postfilter
may be advantageously employed in a variety of technical contexts,
including cell phone transmission and reception technology,
Internet media technology, and other storage or transmission
contexts involving low bit-rate codecs.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic view showing a typical prior art time
domain-postfiltering architecture;
FIG. 2 is an architectural diagram of network linked codecs;
FIG. 3 is a simplified structural schematic of a frequency domain
postfilter according to an embodiment of the invention;
FIGS. 4a, 4b and 4c are structural schematics illustrating
components of a frequency domain formant filter according to an
embodiment of the invention;
FIGS. 5a and 5b are structural schematics illustrating components
of a frequency domain formant filter according to an alternative
embodiment of the invention;
FIGS. 6a and 6b are flow charts demonstrating steps executed in
performing postfiltering according to an embodiment of the
invention; and
FIG. 7 is a simplified schematic illustrating a computing device
architecture employed by a computing device upon which an
embodiment of the invention may be executed.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
The present invention is generally directed to a method and system
of performing postfiltering for improving speech quality, in which
a postfilter is derived from a non-linear transformation of a set
of LPC coefficients in the frequency domain. The derived postfilter
is applied by multiplying the synthesized speech signal by formant
filter gains in the frequency domain. In one embodiment, the
invention is implemented in a decoder for postfiltering a
synthesized speech signal. According to alternate embodiments of
the invention, the LPC coefficients used for deriving the
postfilter may be transmitted from an encoder or may be
independently derived from the synthesized speech in the
decoder.
Although it is not required, the present invention may be
implemented using instructions, such as program modules, that are
executed by a computer. Generally, program modules include
routines, objects, components, data structures and the like that
perform particular tasks or implement particular abstract data
types. The term "program" includes one or more program modules.
The invention may be implemented on a variety of types of machines,
including cell phones, personal computers (PCs), hand-held devices,
multi-processor systems, microprocessor-based programmable consumer
electronics, network PCs, minicomputers, mainframe computers and
the like. The invention may also be employed in a distributed
system, where tasks are performed by components that are linked
through a communications network. In a distributed system,
cooperating modules may be situated in both local and remote
locations.
An exemplary telephony system in which an embodiment of the
invention may be used is described with reference to FIG. 2. The
telephony system comprises codecs 200, 220 communicating with one
another over a network 210, represented by a cloud. Network 210 may
include many well-known components, such as routers, gateways,
hubs, etc. and may allow the codecs 200 to communicate via wired
and/or wireless media. Each codec 200, 220 in general comprises an
encoder 201, a decoder 202 and a postfilter 203.
Codecs 200 and 220 preferably also contain or are associated with a
communication connection that allows the hosting device to
communicate with other devices. A communication connection is an
example of a communication medium. Communication media typically
embody computer readable instructions, data structures, program
modules or other data in a modulated data signal such as a carrier
wave or other transport mechanism and include any information
delivery media. The term computer readable media as used herein
includes both storage media and communication media. The codec
elements described herein may reside entirely in a computer
readable medium. Codecs 200 and 220 may also be associated with
input and output devices such as will be discussed in general later
in this specification.
Referring to FIG. 3, an exemplary postfilter 303 on which the
system described herein may be implemented is shown. In its most
basic configuration, the postfilter 303 utilizes an input
synthesized speech signal S(n) and LPC coefficients .alpha., in
conjunction with a frequency domain formant filter 310. The
postfilter may also have additional features or functionality. For
example, a pitch filter 320 and a gain controller 330 are
preferably also implemented and utilized as will be described
hereinafter.
It is known that the encoding and decoding of a speech signal
typically will introduce unwanted noise into the signal. In the
signal frequency spectrum, such noise overlaps the speech signal
and is particularly audible to humans in valley regions between
consecutive formants. A properly designed and implemented
postfilter will aid in removing this unwanted noise. An ideal
postfilter is one that has a frequency response that follows the
frequency spectrum of the signal of interest. Most current codecs
are based on the principle of linear prediction, wherein the
coefficients of the linear prediction follow the signal frequency
spectrum. In addition to other innovative procedures to be
discussed, the invention takes advantage of this relationship to
derive a speech postfilter, although the invention also allows for
the independent generation of LPC parameters.
There are a wide variety of ways in which frequency domain
postfiltering may be performed in accordance with the invention.
According to one embodiment, frequency domain postfiltering is
performed sequentially within the postfilter. Referring to FIG. 4a,
the frequency domain formant filter 410 comprises a Fourier
transformation module 411, a formant filtering module 412 and an
inverse Fourier transformation module 413. The Fourier
transformation and the inverse Fourier transformation modules are
available to the formant filtering module 412 to transfer signals
between the time domain and the frequency domain, as will be
appreciated by those of skill in the art. The Fourier and inverse
Fourier transformations of the transformation modules 411 and 413
are preferably executed according to the standard Discrete Fourier
Transformation (DFT).
The formant filtering module 412 generates frequency domain gains
and filters the input synthesized speech signal by applying the
generated gains before transforming the subject signal back to the
time domain. FIG. 4b further illustrates the components of the
formant filtering module 412, which comprises a LPC tilt
computation module 415, a LPC tilt compensation module 420, a gain
computation module 430 and a gain application module 440. The
operation of these modules is described in greater detail below
with respect to FIG. 6, but will be described here briefly as
well.
In general, an encoded LPC spectrum has a tilted background. This
tilt may result in unacceptable signal distortion if used to
compute the postfilter without tilt compensation. In particular,
this tilted background could be undesirably amplified during
postfiltering when the postfilter involves a non-linear
transformation as in the present invention. Application of such a
transformation to a tilted spectrum would have the effect of
nonlinearly transforming the tilt as well, making it more difficult
to later obtain a properly non-tilted spectrum. Thus it is
preferable to remove the background tilt of the spectrum prior to
the nonlinear transformation. According to the invention, the tilt
compensation module 420 properly removes the tilted background
according to the tilt estimated by the LPC spectrum tilt
computation module 415.
The gain computation module 430 calculates the frequency domain
formant filter gains including magnitude and phase response. At
this point, the gain application module 440 applies the gains
multiplicatively to the speech signal in the frequency domain.
Referring to FIG. 4c, the gain computation module comprises a time
domain LPC representation module 431, a modeling module 432, a LPC
non-linear transformation module 433, a phase computation module
434, a gain combination module 435, and an anti-aliasing module
436.
LPC representation module 431 creates a time domain vector
representation of the LPC spectrum, after which the vector is
transformed into the frequency domain for further processing. The
modeling module 432 models the frequency domain vector based on one
of a number of suitable models known to those of skill in the art.
In an embodiment of the invention, the inverse of the LPC spectrum
is used to calculate the gains.
The LPC non-linear transformation module 433 calculates the
magnitude of the formant filter gains by conducting a non-linear
transformation of the magnitude of the inverse LPC spectrum.
According to one embodiment of the invention, a scaling function
with a scaling factor of between 0 and 1 is used as a non-linear
transformation function, as will be described in greater detail
below. The parameters in the scaling function are adjustable
according to dynamic environments, for example, according to the
type of input speech signal and the encoding rate. The phase
computation module 434 calculates the phase response for the
formant filter gains. According to one embodiment, the phase
computation module 434 calculates the phase response via the
Hilbert transform, in particular, the phase shifter. Other phase
calculators, for example the Cotangent transform implementation of
the Hilbert transform may alternatively be used. Using the
magnitude and the phase of the formant filter gains provided by the
LPC non-linear transformation module 433 and the phase computation
module 434, the gain combination module 435 generates the gains in
the frequency domain. An anti-aliasing module 436 is preferably
provided to avoid aliasing when postfiltering the signal. It is
preferred, but not essential, to conduct the anti-aliasing
operation in the time domain.
According to the invention, the frequency domain postfilter is
derived from the LPC spectrum and generates, for example, the
frequency domain formant gains, wherein the derivation involves a
sequence of mathematic procedures. It may be desirable to provide a
separate calculation unit that is responsible for all or a portion
of the mathematical processing. In another embodiment of the
invention, a separate LPC evaluation unit is provided to derive the
LPC coefficients as shown in FIG. 5.
Referring to FIG. 5, the frequency domain formant filter 500
comprises a Fourier transformation module 511, an inverse Fourier
transformation module 513, a gain application module 540 and a LPC
evaluation unit 521. The Fourier transformation module 511, inverse
Fourier transformation module 513 and the gain application module
540 may be the same as the modules referred to by similar numbers
in FIG. 4. According to the invention, the LPC evaluation unit 521
comprises a LPC tilt computation module 510, a LPC tilt
compensation module 520 and a gain computation module 530, wherein
these components may be same as the components referenced by the
similar numbers in FIG. 4.
In operation, the alternative embodiment described in FIG. 5 varies
slightly from the embodiment illustrated by way of FIG. 4. In
particular, the gain application module 540 receives as input a
synthesized speech signal and provides as output a filtered
synthesized speech signal. Fourier and inverse Fourier transform
modules 511 and 513 are available to the gain application module
for transformation of the pre-filtered speech signal into the
frequency domain, and for transformation of the post-filtered
speech signal into the time domain. LPC evaluation unit 521
receives or calculates the LPC coefficients, accesses the
transformation modules 511 and 513 when necessary for
transformation between the time and frequency domains, and returns
computed gains to the gain application module 540.
Referring to FIGS. 6a and 6b, exemplary steps taken to perform
postfiltering in accordance with an embodiment of the invention are
illustrated. The synthesized speech signal S(n) and the LPC
coefficients .alpha..sub.1, are received at step 601. Because an
encoded LPC spectrum generally has a tilted background that induces
extra distortion when used directly to compute formant postfilter,
it is preferable to first compute and correct for any spectral
tilt. Uncorrected tilt may be undesirably amplified during the
computation of the postfilter, especially when such computation
involves a non-linear transformation. Accordingly, at steps 603 and
605, respectively, the LPC spectrum tilt is calculated and the
spectrum compensated therefor. Exemplary mathematic procedures
usable to execute these steps are as follows. Those of skill in the
art will recognize that the following mathematical procedures may
be modified in arrangement and detail and yet achieve the same
result. For LPC coefficients .alpha..sub.i (i=0,1 . . . P and
.alpha..sub.0 =1), where P is the order of the LPC polynomial
coefficients, the tilt .mu. of the LPC spectrum is defined as:
##EQU1##
where R(1) and R(0) are autocorrelation values of the LPC
parameters defined by ##EQU2##
The LPC order P is selected depending on the sample frequency as
will be apparent to those of skill in the art. In this embodiment,
P=10 is used for 8 kHz and 11.025 kHz sampling rates, while P=16 is
used for 16 kHz and 22.05 kHz sampling rates. Given the calculated
tilt .mu., the LPC coefficients .alpha..sub.1 are compensated as
follows: ##EQU3##
At step 607, a vector representation denoted by A of the tilt
compensated LPC .alpha..sub.1 in the time domain is obtained by
zero-padding to form a convenient size vector. An exemplary length
for such a vector is 128, although other similar or quite different
vector lengths may equivalently be employed.
At steps 609 to 623 the formant postfilter gains including
magnitude and phase response are calculated. In particular, at step
609, the vector A is transformed to a frequency domain vector A'(k)
via a Fourier transformation. At step 613, the frequency domain
vector A'(k) is modified by inversing the magnitude of the A'(k)
and converting to log scale (dB). The transfer function according
to this step is denoted by H(k). For mathematical efficiency and
convenience, H(k) is first normalized in step 615 to H(k), as in
the following example: ##EQU4##
where H.sub.max (k) and H.sub.min (k) represent the maximum and the
minimum values of H(k), respectively.
In step 615, the normalized function H(k) is non-linearly
transformed through a scaling function such as the following:
##EQU5##
where c is a constant. An exemplary value of c is 1.47 for a voiced
signal, and 1.3 for an unvoiced signal. The scaling factor .gamma.
may be adjusted according to dynamic environmental conditions. For
example, different types of speech coders and encoding rates may
optimally use different values for this constant. An exemplary
value for the scaling factor .gamma. is 0.25, although other
scaling factors may yield acceptable or better results. Even though
the present invention has been described as utilizing the above
scaling function for the step of non-linear transformation, other
non-linear transformation functions may alternatively be used. Such
functions include suitable exponential functions and polynomial
functions.
The function T(k) obtained in step 615 is then used to estimate the
phase response of the gain. In accordance with the invention, steps
617 to 623 implement the Hilbert phase shifter to calculate the
phase response .theta.(k) of the gain. In particular, at step 617,
the function T(k) is transferred into the time domain by conducting
the Fourier transformation, since the Hilbert phase shifter is
conducted in the time domain. At step 619, The phase response
.theta.(n) is obtained by multiplying T(n) with j, wherein j is
defined as j.sup.2 =-1. At step 621, the calculated phase response
of the gains .theta.(n) are transformed into the frequency domain
phase response .theta.(k) for further processing in the frequency
domain.
At step 623, the frequency domain formant filter gain F(k) is
obtained by combining the magnitude and phase components as
follows: ##EQU6##
where q and g are constants defined as: ##EQU7##
wherein ln is the natural logarithm.
Steps 625 to 631 are executed to conduct anti-aliasing in the time
domain. In particular, in step 625, the frequency domain gain F(k)
is transformed to a time domain gain f(n) through execution of an
inverse Fourier transformation. That is, the Inverse Fourier
transformation of F(k) equals f(n). In step 627, a second function
g(n) is defined by zeroing the coefficients of f(n) according to
the Fourier transformation length N and the input speech segment
length M as follows: ##EQU8##
Step 629 entails applying a standard normalization procedure to
g(n) as follows: ##EQU9##
Finally, the frequency domain gain G(k) after anti-aliasing is
obtained by transferring the time domain function g.sub.n (n) into
the frequency domain through a Fourier transformation in step 631.
That is, the Fourier transformation of g.sub.n (n) equals G(k).
Having calculated the frequency domain formant gain G(k), steps 633
to 637 are executed to effect filtering of the input synthesized
speech signal S(n). In particular, in step 633, the signal S(n) is
first transferred into a frequency domain signal S(k). Recalling
that postfiltering in the frequency domain is implemented by
multiplication of the signal by a gain for each frequency, S(k) is
multiplied in step 635 by the frequency domain formant filter gains
G(k) and the postfiltered speech signal S'(k) is then obtained. By
then transforming S'(k) into the time domain in step 637, a
postfiltered speech signal S'(n) is obtained.
With reference to FIG. 7, one exemplary system for implementing
embodiments of the invention includes a computing device, such as
computing device 700. In its most basic configuration, computing
device 700 typically includes at least one processing unit 702 and
memory 704. Depending on the exact configuration and type of
computing device, memory 704 may be volatile (such as RAM),
non-volatile (such as ROM, flash memory, etc.) or some combination
of the two. This most basic configuration is illustrated in FIG. 7
by line 706. Additionally, device 700 may also have additional
features/functionality. For example, device 700 may also include
additional storage (removable and/or non-removable) including, but
not limited to, magnetic or optical disks or tape. Such additional
storage is illustrated in FIG. 7 by removable storage 708 and
non-removable storage 710. Computer storage media includes volatile
and nonvolatile, removable and non-removable media implemented in
any method or technology for storage of information such as
computer readable instructions, data structures, program modules or
other data. Memory 704, removable storage 708 and non-removable
storage 710 are all examples of computer storage media. Computer
storage media includes, but is not limited to, RAM, ROM, EEPROM,
flash memory or other memory technology, CDROM, digital versatile
disks (DVD) or other optical storage, magnetic cassettes, magnetic
tape, magnetic disk storage or other magnetic storage devices, or
any other medium which can be used to store the desired information
and which can be accessed by device 700. Any such computer storage
media may be part of device 700.
Device 700 may also contain one or more communications connections
712 that allow the device to communicate with other devices.
Communications connections 712 are an example of communication
media. Communication media typically embodies computer readable
instructions, data structures, program modules or other data in a
modulated data signal such as a carrier wave or other transport
mechanism and includes any information delivery media. The term
"modulated data signal" means a signal that has one or more of its
characteristics set or changed in such a manner as to encode
information in the signal. By way of example, and not limitation,
communication media includes wired media such as a wired network or
direct-wired connection, and wireless media such as acoustic, RF,
infrared and other wireless media. As discussed above, the term
computer readable media as used herein includes both storage media
and communication media.
Device 700 may also have one or more input devices 714 such as
keyboard, mouse, pen, voice input device, touch input device, etc.
One or more output devices 716 such as a display, speakers,
printer, etc. may also be included. All these devices are well
known in the art and need not be discussed at greater length
here.
It will be appreciated by those of skill in the art that a new and
useful method and system of performing postfiltering have been
described herein. In view of the many possible embodiments to which
the principles of this invention may be applied, however, it should
be recognized that the embodiments described herein with respect to
the drawing figures are meant to be illustrative only and should
not be taken as limiting the scope of invention. For example, those
of skill in the art will recognize that the illustrated embodiments
can be modified in arrangement and detail without departing from
the spirit of the invention. For example, the invention is
described as employing a scaling function with the scaling factor
being between 0 and 1 for non-linear transformation. However, other
transformation functions and factors may also be employed. For
example, exponential and polynomial functions may also be used
within the invention. Further, although the Hilbert phase shifter
is specified for calculating the phase response of the gain, other
techniques for calculating the phase response of a function may
also be used, such as the Cotangent transform technique. In
conducting time domain to frequency domain transformation, this
specification prescribes the DFT, but other transformation
techniques may equivalently be employed, such as the Fast Fourier
Transformation (FFT), or even a standard Fourier transformation.
Although the invention is described in terms of software modules or
components, those skilled in the art will recognize that such may
be equivalently replaced by hardware components. Therefore, the
invention as described herein contemplates all such embodiments as
may come within the scope of the following claims and equivalents
thereof.
* * * * *